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The complete guide to Claude Code for Python automation in 2026. Learn setup, workflow automation, data processing, API integration, hooks, and real-world Python scripts. No coding experience required.
Jun 27, 2026
Agent skills are reusable instruction packages that teach your AI coding assistant how to handle a specific class of task. This guide walks you through building one from scratch — from the blank SKILL.md to a published package others can install.
Jun 25, 2026
Stop staring at your terminal. Make Claude ding on approval, chime when tasks finish, or mount a physical traffic light on your monitor — step-by-step hooks plus the viral DIY hardware trend.
Jun 20, 2026
Every developer asking "how do I actually build one of these loops?" gets the same answer: five components, three levels, and one feedback gate that says no. This guide walks you from a blank terminal to a working autonomous agent loop in under an hour — no orchestration framework required.
TL;DR: Claude Code is Anthropic's agentic CLI tool that automates Python development through a write-run-fix loop—writing code, executing it, reading errors, fixing issues, and rerunning autonomously. This complete guide covers installation and setup, CLAUDE.md configuration, workflow automation patterns (data processing, API integration, web scraping), hooks for testing and formatting, and real-world Python automation projects. Perfect for beginners: no deep coding knowledge required, just clear problem articulation. Learn to build automated scripts 10x faster than traditional coding.
Claude Code is Anthropic's agentic command-line interface (CLI) tool developed to assist developers with Python automation, refactoring, documentation, and debugging directly in the terminal.
Traditional Python development:
Developer writes code → Runs script → Error occurs →
Reads traceback → Googles solution → Fixes code → Reruns →
Another error → Repeat...
Claude Code automates this entire loop:
Claude writes code → Executes automatically →
Reads traceback → Edits code → Reruns →
Iterates until it works → You watch it happen
| Feature | Description |
|---|---|
| Autonomous Execution | Writes and runs code without manual intervention |
| Error Resolution | Reads tracebacks, understands errors, fixes code |
| Multi-iteration | Goes through several fix attempts automatically |
| Terminal Integration | Works directly in your command line |
| Project Context | Remembers your project structure and goals |
| File Operations | Creates, edits, and manages Python files |
Sources: DataCamp Tutorial, PyDevTools Complete Guide
Traditional Approach:
Claude Code Approach:
Result: 85% time reduction
Claude Code excels at the write-run-fix loop:
"It automates the write-run-fix loop. It writes code, executes it, reads the traceback when something fails, edits the code, and reruns. You watch the cycle happen in your terminal. Claude Code can go through several iterations in the time it would take to manually interpret an error message and look up the fix."
No Need for:
You Just Need:
Sources: Medium - End-to-End Guide, Real Python - Getting Started
Required:
Operating Systems:
Via npm (Recommended):
# Install globally
npm install -g @anthropic-ai/claude-code
# Verify installation
claude-code --version
Alternative: Via pip (if available):
pip install anthropic-claude-code
Get Your API Key:
sk-ant-api...)Set Up Authentication:
# Option 1: Environment variable (recommended)
export ANTHROPIC_API_KEY='your-api-key-here'
# Add to ~/.bashrc or ~/.zshrc for persistence
echo 'export ANTHROPIC_API_KEY="your-key"' >> ~/.zshrc
# Option 2: Interactive setup
claude-code auth login
# Test Claude Code
claude-code --help
# Run test query
claude-code "print hello world in Python"
Expected Output:
# Claude creates and runs:
print("Hello, World!")
# Output: Hello, World!
Sources: DataCamp Setup, PyDevTools Tutorial
CLAUDE.md is a markdown file in your project root that gives Claude Code standing instructions it reads at the start of every session.
# Project: Data Processing Automation
## Overview
This project automates daily sales data processing, cleaning, and report generation.
## Tech Stack
- Python 3.10
- pandas for data manipulation
- requests for API calls
- openpyxl for Excel generation
## Code Style
- Use type hints for all functions
- Follow PEP 8 style guide
- Use descriptive variable names
- Add docstrings to all functions
- Prefer list comprehensions over loops where readable
## Project Structure
project/ ├── data/ # Raw CSV files ├── processed/ # Cleaned data ├── reports/ # Generated reports ├── scripts/ # Python automation scripts └── tests/ # Unit tests
## Common Tasks
1. Data cleaning: Remove duplicates, handle missing values
2. Data transformation: Convert currencies, normalize dates
3. Report generation: Create Excel summary with charts
4. API integration: POST processed data to dashboard
## Dependencies
Install via: `pip install pandas requests openpyxl pytest`
## Testing
Run tests before committing: `pytest tests/`
## Deployment
Scripts run via cron job daily at 6 AM UTC
1. Workflow Instructions
## Automation Workflow
### Daily Sales Processing
1. Fetch CSVs from `data/` directory
2. Clean data:
- Remove rows where `amount < 0`
- Fill missing `customer_name` with "Unknown"
- Convert `date` to ISO format
3. Group by product category
4. Generate Excel report with:
- Summary sheet (totals, averages)
- Detailed sheet (all records)
- Charts sheet (bar chart of sales by category)
5. Save to `reports/sales-YYYY-MM-DD.xlsx`
6. POST summary to API endpoint: `https://api.example.com/sales`
2. Error Handling Guidelines
## Error Handling
- Always use try-except blocks for file operations
- Log all errors to `logs/errors.log`
- Send email notification on critical failures
- Retry API calls 3 times with exponential backoff
- Validate data before processing (check dtypes, nulls, ranges)
3. Performance Optimizations
## Performance
- Use pandas vectorized operations (avoid iterrows)
- Process large files in chunks (chunksize=10000)
- Cache API responses for 1 hour
- Use multiprocessing for independent tasks
- Profile scripts with cProfile before optimization
4. Security Guidelines
## Security
- Never hardcode API keys (use environment variables)
- Store credentials in `.env` file (add to .gitignore)
- Validate all user inputs
- Use parameterized queries for database operations
- Encrypt sensitive data before storing
Sources: DataCamp Hooks Tutorial, Leon Furze Automation Guide
Use Case: Daily sales data cleaning and transformation
What We're Building:
Claude Code Prompt:
Create a Python script that:
1. Reads all CSV files from the 'data/' directory
2. For each CSV:
- Load using pandas
- Remove duplicate rows
- Fill missing 'price' values with column median
- Convert 'date' column to datetime format
- Calculate 'revenue' = quantity * price
3. Combine all data into one DataFrame
4. Create Excel file with two sheets:
- 'Summary': Total sales, average price, top 10 products
- 'Details': All cleaned records
5. Save to 'reports/sales-report-YYYY-MM-DD.xlsx'
Requirements:
- Use pandas and openpyxl
- Add error handling for missing files
- Log processing status
- Generate report filename with current date
Claude Code Process:
$ claude-code "Execute the data processing task"
[Claude Code working...]
✓ Created data_processor.py
✓ Installed dependencies (pandas, openpyxl)
✓ Wrote CSV reading logic
✓ Executed script
✗ Error: FileNotFoundError: 'data/' directory not found
[Claude Code auto-fixing...]
✓ Added directory existence check
✓ Created 'data/' directory if missing
✓ Reran script
✓ Success! Generated reports/sales-report-2026-06-09.xlsx
Script complete. Processing time: 2.3 seconds
Processed 1,247 records from 3 CSV files
Generated Script (data_processor.py):
import pandas as pd
import os
from datetime import datetime
from pathlib import Path
import logging
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
def ensure_directories():
"""Create necessary directories if they don't exist"""
Path('data').mkdir(exist_ok=True)
Path('reports').mkdir(exist_ok=True)
logging.info("Directories verified")
def process_csv_files():
"""Process all CSV files in data directory"""
data_dir = Path('data')
csv_files = list(data_dir.glob('*.csv'))
if not csv_files:
logging.warning("No CSV files found in data/ directory")
return
all_data = []
for csv_file in csv_files:
logging.info(f"Processing {csv_file.name}")
# Read CSV
df = pd.read_csv(csv_file)
# Remove duplicates
df = df.drop_duplicates()
# Fill missing prices with median
if 'price' in df.columns:
median_price = df['price'].median()
df['price'] = df['price'].fillna(median_price)
# Convert date column
if 'date' in df.columns:
df['date'] = pd.to_datetime(df['date'])
# Calculate revenue
df.columns df.columns:
df[] = df[] * df[]
all_data.append(df)
combined_df = pd.concat(all_data, ignore_index=)
logging.info()
combined_df
():
today = datetime.now().strftime()
filename =
pd.ExcelWriter(filename, engine=) writer:
summary = {
: df[].() df.columns ,
: df[].mean() df.columns ,
: (df)
}
summary_df = pd.DataFrame([summary])
summary_df.to_excel(writer, sheet_name=, index=)
df.columns df.columns:
top_products = (df.groupby()[]
.()
.sort_values(ascending=)
.head())
top_products.to_excel(writer, sheet_name=)
df.to_excel(writer, sheet_name=, index=)
logging.info()
filename
():
:
ensure_directories()
df = process_csv_files()
df df.empty:
filename = generate_excel_report(df)
()
:
()
Exception e:
logging.error()
__name__ == :
main()
Key Learning:
Use Case: Fetch data from REST API, process, and POST results
Claude Code Prompt:
Build a Python script that automates API data flow:
1. GET weather data from OpenWeatherMap API
- Endpoint: https://api.openweathermap.org/data/2.5/forecast
- For cities: New York, London, Tokyo
- API key from environment variable
2. Process the data:
- Extract: city, date, temperature, conditions
- Convert temperature from Kelvin to Celsius
- Format date as YYYY-MM-DD
3. POST processed data to our internal API
- Endpoint: https://internal.example.com/api/weather
- Include API authentication header
4. Error handling:
- Retry failed requests 3 times
- Log all API calls
- Email notification if all retries fail
5. Run daily via cron job (6 AM UTC)
Generated Script Structure:
import requests
import os
from datetime import datetime
import time
import logging
import smtplib
from email.message import EmailMessage
class WeatherAutomation:
def __init__(self):
self.weather_api_key = os.getenv('OPENWEATHER_API_KEY')
self.internal_api_key = os.getenv('INTERNAL_API_KEY')
self.cities = ['New York', 'London', 'Tokyo']
def fetch_weather(self, city, retries=3):
"""Fetch weather with retry logic"""
url = "https://api.openweathermap.org/data/2.5/forecast"
params = {'q': city, 'appid': self.weather_api_key}
for attempt in range(retries):
try:
response = requests.get(url, params=params, timeout=10)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
logging.warning(f"Attempt {attempt+1} failed: {e}")
if attempt < retries - 1:
time.sleep(2 ** attempt) # Exponential backoff
else:
raise
def ():
processed = []
item raw_data.get(, []):
processed.append({
: raw_data[][],
: datetime.fromtimestamp(item[]).strftime(),
: (item[][] - , ),
: item[][][]
})
processed
():
url =
headers = {: }
response = requests.post(url, json=data, headers=headers)
response.raise_for_status()
logging.info()
():
():
:
all_data = []
city .cities:
raw_data = .fetch_weather(city)
processed_data = .process_data(raw_data)
all_data.extend(processed_data)
.post_to_internal_api(all_data)
logging.info()
Exception e:
logging.error()
.send_error_notification((e))
__name__ == :
automation = WeatherAutomation()
automation.run()
Use Case: Scrape product prices from e-commerce site daily
Claude Code Prompt:
Create a web scraping script that:
1. Scrapes prices from 5 product pages
2. Stores data in SQLite database with timestamp
3. Compares with yesterday's prices
4. Generates alert if price drops > 10%
5. Sends Telegram notification with deal alerts
Use BeautifulSoup and requests.
Handle anti-scraping measures (delays, user-agent).
Generated Script Highlights:
from bs4 import BeautifulSoup
import requests
import sqlite3
import time
from datetime import datetime
import random
class PriceScraper:
def __init__(self):
self.db_path = 'prices.db'
self.setup_database()
def setup_database(self):
"""Initialize SQLite database"""
conn = sqlite3.connect(self.db_path)
conn.execute('''
CREATE TABLE IF NOT EXISTS prices (
id INTEGER PRIMARY KEY,
product_url TEXT,
product_name TEXT,
price REAL,
timestamp DATETIME
)
''')
conn.commit()
conn.close()
def scrape_price(self, url):
"""Scrape single product with anti-detection"""
headers = {
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36'
}
# Random delay (1-3 seconds)
time.sleep(random.uniform(1, 3))
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, 'html.parser')
# Extract price (adapt selectors to target site)
price_element = soup.select_one('.price-current')
price = float(price_element.text.strip().replace('$', ''))
name_element = soup.select_one('.product-title')
name = name_element.text.strip()
return {'name': name, 'price': price, 'url': url}
():
conn = sqlite3.connect(.db_path)
cursor = conn.execute(, (product_url,))
previous = cursor.fetchone()
conn.close()
previous:
old_price = previous[]
drop_percent = ((old_price - current_price) / old_price) *
drop_percent drop_percent >
():
Sources: Medium Learning Path, Paul GP Researcher Guide
Hooks are automated triggers that execute commands in response to events.
1. Pre-Commit Hooks
Run before git commits to ensure code quality:
# In CLAUDE.md
## Hooks
### pre-commit
- Run unit tests: `pytest tests/`
- Run linter: `flake8 *.py`
- Check type hints: `mypy *.py`
- If any fail, block commit
2. Post-Edit Hooks
Format code after Claude makes changes:
### post-edit
- Auto-format with Black: `black *.py`
- Sort imports: `isort *.py`
- Update docstrings: `pydocstyle *.py`
3. On-Error Hooks
Notify when errors occur:
### on-error
- Log to file: append error to `logs/errors.log`
- Send Slack notification: curl webhook with error details
- Create GitHub issue (for recurring errors)
4. Scheduled Hooks
Run tasks periodically:
### scheduled (daily at 6 AM)
- Backup database: `python backup_db.py`
- Clean old logs: `find logs/ -mtime +30 -delete`
- Generate reports: `python generate_reports.py`
Method 1: CLAUDE.md Configuration
## Automation Hooks
### Pre-Commit Quality Checks
```bash
#!/bin/bash
echo "Running pre-commit checks..."
# Run tests
pytest tests/ || exit 1
# Check code style
flake8 *.py || exit 1
# Type checking
mypy *.py || exit 1
echo "✓ All checks passed"
black *.py
isort *.py
echo "✓ Code formatted"
# Add to crontab: crontab -e
0 6 * * * cd /path/to/project && python automate.py
**Method 2: Hooks Configuration File**
Create `.claude/hooks.yml`:
```yaml
hooks:
pre-commit:
- name: "Run Tests"
command: "pytest tests/"
blocking: true
- name: "Lint Code"
command: "flake8 *.py"
blocking: true
post-edit:
- name: "Format Code"
command: "black *.py"
blocking: false
on-error:
- name: "Log Error"
command: "echo '$(date): Error occurred' >> logs/errors.log"
scheduled:
- name: "Daily Backup"
schedule: "0 6 * * *" # Cron syntax
command: "python backup.py"
Sources: DataCamp Hooks Guide
Don't:
"Build a complete data pipeline with ETL, machine learning,
API integration, real-time dashboards, and automated alerts"
Do:
Session 1: "Process a single CSV file"
Session 2: "Add data validation"
Session 3: "Generate simple report"
Session 4: "Add API integration"
Session 5: "Automate with scheduling"
Vague:
"Fix the data thing"
Specific:
"In data_processor.py, fix the function clean_sales_data()
to handle null values in the 'amount' column by:
1. Replacing nulls with 0 if 'status' is 'refund'
2. Replacing nulls with median if 'status' is 'sale'
3. Logging how many nulls were replaced"
Always Check:
Keep your project instructions current:
## Recent Changes (Update Log)
### 2026-06-09
- Added API rate limiting (max 100 calls/hour)
- Changed database from SQLite to PostgreSQL
- New requirement: All prices must be in USD
### 2026-06-08
- Migrated from pandas 1.x to 2.x
- Updated data cleaning logic for new CSV format
# .gitignore
.env
*.pyc
__pycache__/
logs/
.claude/cache/
# README.md
## Running with Claude Code
1. Install dependencies: `pip install -r requirements.txt`
2. Set environment variables (see .env.example)
3. Run automation: `claude-code "run daily automation"`
# tests/test_data_processor.py
import pytest
from data_processor import clean_sales_data
def test_clean_sales_data_removes_duplicates():
input_data = [
{'id': 1, 'amount': 100},
{'id': 1, 'amount': 100}, # duplicate
{'id': 2, 'amount': 200}
]
result = clean_sales_data(input_data)
assert len(result) == 2
def test_clean_sales_data_handles_nulls():
input_data = [{'id': 1, 'amount': None}]
result = clean_sales_data(input_data)
assert result[0]['amount'] == 0 # or median, depending on logic
Run tests with Claude Code:
claude-code "run pytest and fix any failing tests"
Problem:
Error: ANTHROPIC_API_KEY environment variable not set
Solution:
# Verify environment variable
echo $ANTHROPIC_API_KEY
# If empty, set it:
export ANTHROPIC_API_KEY='sk-ant-api...'
# Make permanent (add to ~/.zshrc or ~/.bashrc):
echo 'export ANTHROPIC_API_KEY="your-key"' >> ~/.zshrc
source ~/.zshrc
Problem:
ModuleNotFoundError: No module named 'pandas'
Solution:
# Claude Code should auto-install, but if not:
pip install pandas
# Or create requirements.txt and install all:
pip install -r requirements.txt
# Tell Claude about dependencies in CLAUDE.md:
## Dependencies
Install via: `pip install pandas requests openpyxl`
Problem:
PermissionError: [Errno 13] Permission denied: 'output.xlsx'
Solution:
# Check file permissions
ls -la output.xlsx
# Fix permissions
chmod 644 output.xlsx
# Or delete locked file
rm output.xlsx
# Tell Claude Code:
claude-code "fix the permission error by checking if file is locked before writing"
Problem: Code enters infinite loop or takes too long
Solution:
# Interrupt Claude Code:
Ctrl+C
# Restart with clearer instructions:
claude-code "In script.py, add a max_iterations=10 limit
to the while loop to prevent infinite loops"
Problem:
Error: API rate limit exceeded
Solution:
# Add rate limiting to your code
import time
def rate_limited_api_call(url):
time.sleep(1) # Wait 1 second between calls
response = requests.get(url)
return response
# Or use CLAUDE.md:
## API Guidelines
- Max 100 API calls per minute
- Add 1-second delay between calls
- Implement exponential backoff for failures
Project Structure:
project/
├── CLAUDE.md
├── main.py
├── data_processor.py
├── api_client.py
├── config.py
├── requirements.txt
└── tests/
├── test_processor.py
└── test_api.py
Coordination via CLAUDE.md:
## File Structure
- `main.py`: Entry point, orchestrates workflow
- `data_processor.py`: Data cleaning and transformation
- `api_client.py`: API calls with retry logic
- `config.py`: Configuration and environment variables
## Workflow
1. main.py imports data_processor and api_client
2. data_processor handles all pandas operations
3. api_client manages external API interactions
4. Keep files < 300 lines for maintainability
# db_manager.py - Generated by Claude Code
import sqlite3
from contextlib import contextmanager
class DatabaseManager:
def __init__(self, db_path='automation.db'):
self.db_path = db_path
self.setup_database()
@contextmanager
def get_connection(self):
"""Context manager for database connections"""
conn = sqlite3.connect(self.db_path)
try:
yield conn
conn.commit()
except Exception as e:
conn.rollback()
raise
finally:
conn.close()
def setup_database(self):
"""Create tables if they don't exist"""
with self.get_connection() as conn:
conn.execute('''
CREATE TABLE IF NOT EXISTS automation_runs (
id INTEGER PRIMARY KEY,
script_name TEXT,
status TEXT,
records_processed INTEGER,
execution_time REAL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
)
''')
def log_run(self, script_name, status, records, exec_time):
"""Log automation execution"""
with self.get_connection() as conn:
conn.execute('''
INSERT INTO automation_runs
(script_name, status, records_processed, execution_time)
VALUES (?, ?, ?, ?)
''', (script_name, status, records, exec_time))
# config.py - Generated by Claude Code
import os
from dotenv import load_dotenv
load_dotenv()
class Config:
# API Keys
OPENWEATHER_API_KEY = os.getenv('OPENWEATHER_API_KEY')
INTERNAL_API_KEY = os.getenv('INTERNAL_API_KEY')
# Database
DB_PATH = os.getenv('DB_PATH', 'automation.db')
# Paths
DATA_DIR = 'data/'
REPORTS_DIR = 'reports/'
LOGS_DIR = 'logs/'
# API Settings
API_TIMEOUT = 10
API_RETRIES = 3
API_BACKOFF_FACTOR = 2
# Email Alerts
SMTP_HOST = os.getenv('SMTP_HOST')
SMTP_PORT = int(os.getenv('SMTP_PORT', 587))
ALERT_EMAIL = os.getenv('ALERT_EMAIL')
@classmethod
def validate(cls):
"""Validate required config"""
required = ['OPENWEATHER_API_KEY', 'INTERNAL_API_KEY']
missing = [key for key in required if not getattr(cls, key)]
if missing:
raise ValueError(f"Missing config: {', '.join(missing)}")
# Usage in your scripts:
from config import Config
Config.validate()
api_key = Config.OPENWEATHER_API_KEY
Complete workflow built with Claude Code:
Requirement:
"Automate daily e-commerce analytics: fetch orders from Shopify API, process data, calculate metrics, generate Excel report, and email to stakeholders."
Claude Code Session:
$ claude-code "Build e-commerce analytics automation as described in requirements.md"
[Claude Code Process:]
✓ Read requirements.md
✓ Created project structure:
- main.py (orchestrator)
- shopify_client.py (API integration)
- analytics_processor.py (metrics calculation)
- report_generator.py (Excel creation)
- email_sender.py (SMTP integration)
- config.py (configuration)
✓ Installed dependencies: shopify-python-api, pandas, openpyxl
✓ Wrote shopify_client.py with pagination support
✓ Executed test run
✗ Error: ShopifyAPI authentication failed
[Auto-fixing...]
✓ Updated authentication to use private app credentials
✓ Reran test
✓ Successfully fetched 1,247 orders
✓ Wrote analytics_processor.py with metrics:
- Total revenue
- Average order value
- Top 10 products
- Sales by category
- Customer acquisition trends
✓ Wrote report_generator.py with Excel formatting
✓ Wrote email_sender.py with HTML email template
✓ Created main.py to orchestrate the workflow
✓ Full test run successful
✓ Generated reports/analytics-2026-06-09.xlsx
✓ Sent email to 3 stakeholders
Automation complete. Total time: 8 minutes.
Generated main.py:
import logging
from datetime import datetime, timedelta
from shopify_client import ShopifyClient
from analytics_processor import AnalyticsProcessor
from report_generator import ReportGenerator
from email_sender import EmailSender
from config import Config
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('logs/automation.log'),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
def main():
try:
# Validate configuration
Config.validate()
logger.info("Configuration validated")
# Initialize clients
shopify = ShopifyClient(Config.SHOPIFY_API_KEY, Config.SHOPIFY_STORE)
processor = AnalyticsProcessor()
report_gen = ReportGenerator()
email_sender = EmailSender(Config.SMTP_HOST, Config.SMTP_PORT)
# Fetch orders (last 7 days)
start_date = datetime.now() - timedelta(days=7)
logger.info(f"Fetching orders since {start_date}")
orders = shopify.fetch_orders(start_date)
logger.info(f"Fetched {len(orders)} orders")
# Process analytics
logger.info("Calculating metrics")
metrics = processor.calculate_metrics(orders)
# Generate report
today = datetime.now().strftime('%Y-%m-%d')
report_path = f'reports/analytics-{today}.xlsx'
logger.info(f"Generating report: {report_path}")
report_gen.create_report(metrics, orders, report_path)
# Send email
logger.info("Sending email")
email_sender.send_report(
recipients=Config.STAKEHOLDER_EMAILS,
report_path=report_path,
metrics=metrics
)
logger.info()
Exception e:
logger.error(, exc_info=)
email_sender.send_error_notification((e))
__name__ == :
main()
Deployment (cron job):
# Run daily at 8 AM
0 8 * * * cd /path/to/project && /usr/bin/python3 main.py >> logs/cron.log 2>&1
Day 1-2:
Day 3-4:
Day 5-7:
Day 8-10:
Day 11-14:
Day 15-21:
Day 22-28:
Week 4 of the bootcamp is dedicated to Python automation with Claude Code:
What You'll Build:
Hands-On Projects:
6 weeks. 12 live sessions. 350,000+ students trained.
Learn from Yash Thakker with personalized guidance and lifetime recording access.
Official Documentation:
Tutorials:
Advanced Guides:
This guide covers Claude Code for Python automation as of June 2026. Claude Code is actively developed; check official documentation for latest features.